docstr: ivy.Array instance method variant of ivy.nanmedian. This method
  simply wraps the function, and so the docstring for ivy.nanmedian also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input array.
  axis
      The axis or axes along which the means are computed.
      The default is to compute the mean of the flattened array.
  keepdims
      If this is set to True, the axes which are reduced are left in the result
      as dimensions with size one. With this option, the result will broadcast
      correctly against the original input array. If the value is anything
      but the default, then keepdims will be passed through to the mean or
      sum methods of sub-classes of ndarray. If the sub-classes methods does
      not implement keepdims any exceptions will be raised.
  overwrite_input
      If True, then allow use of memory of input array a for calculations.
      The input array will be modified by the call to median. This will
      save memory when you do not need to
      preserve the contents of the input array.
      Treat the input as undefined, but it will
      probably be fully or partially sorted.
      Default is False. If overwrite_input
      is True and input array is not already an ndarray,
      an error will be raised.
  out
      optional output array, for writing the result to.
  
  Returns
  -------
  ret
      A new array holding the result. If the input contains integers
  
  Examples
  --------
  With :class:`ivy.array` input and default backend set as `numpy`:
  
  >>> a = ivy.array([[10.0, ivy.nan, 4], [3, 2, 1]])
  >>> a.nanmedian()
      ivy.array(3.)
  >>> a.nanmedian(axis=0)
      ivy.array([6.5, 2. , 2.5])
docstr_tok: ["ivy.Array","instance","method","variant","of","ivy.nanmedian",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.nanmedian","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","Input","array",".","axis","The","axis","or","axes","along","which","the","means","are","computed",".","The","default","is","to","compute","the","mean","of","the","flattened","array",".","keepdims","If","this","is","set","to","True",",","the","axes","which","are","reduced","are","left","in","the","result","as","dimensions","with","size","one",".","With","this","option",",","the","result","will","broadcast","correctly","against","the","original","input","array",".","If","the","value","is","anything","but","the","default",",","then","keepdims","will","be","passed","through","to","the","mean","or","sum","methods","of","sub-classes","of","ndarray",".","If","the","sub-classes","methods","does","not","implement","keepdims","any","exceptions","will","be","raised",".","overwrite_input","If","True",",","then","allow","use","of","memory","of","input","array","a","for","calculations",".","The","input","array","will","be","modified","by","the","call","to","median",".","This","will","save","memory","when","you","do","not","need","to","preserve","the","contents","of","the","input","array",".","Treat","the","input","as","undefined",",","but","it","will","probably","be","fully","or","partially","sorted",".","Default","is","False",".","If","overwrite_input","is","True","and","input","array","is","not","already","an","ndarray",",","an","error","will","be","raised",".","out","optional","output","array",",","for","writing","the","result","to",".","Returns","--","--","--","-","ret","A","new","array","holding","the","result",".","If","the","input","contains","integers","Examples","--","--","--","--","With",":","class",":","`","ivy.array","`","input","and","default","backend","set","as","`","numpy","`",":",">",">",">","a","=","ivy.array","(","[","[","10.0",",","ivy.nan",",","4","]",",","[","3",",","2",",","1","]","]",")",">",">",">","a.nanmedian","(",")","ivy.array","(","3",".",")",">",">",">","a.nanmedian","(","axis=0",")","ivy.array","(","[","6.5",",","2.",",","2.5","]",")"]
code: def nanmedian(
      self: ivy.Array,
      /,
      *,
      axis: Optional[Union[Tuple[int], int]] = None,
      keepdims: bool = False,
      overwrite_input: bool = False,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.nanmedian. This method
      simply wraps the function, and so the docstring for ivy.nanmedian also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input array.
      axis
          The axis or axes along which the means are computed.
          The default is to compute the mean of the flattened array.
      keepdims
          If this is set to True, the axes which are reduced are left in the result
          as dimensions with size one. With this option, the result will broadcast
          correctly against the original input array. If the value is anything
          but the default, then keepdims will be passed through to the mean or
          sum methods of sub-classes of ndarray. If the sub-classes methods does
          not implement keepdims any exceptions will be raised.
      overwrite_input
          If True, then allow use of memory of input array a for calculations.
          The input array will be modified by the call to median. This will
          save memory when you do not need to
          preserve the contents of the input array.
          Treat the input as undefined, but it will
          probably be fully or partially sorted.
          Default is False. If overwrite_input
          is True and input array is not already an ndarray,
          an error will be raised.
      out
          optional output array, for writing the result to.
  
      Returns
      -------
      ret
          A new array holding the result. If the input contains integers
  
      Examples
      --------
      With :class:`ivy.array` input and default backend set as `numpy`:
  
      >>> a = ivy.array([[10.0, ivy.nan, 4], [3, 2, 1]])
      >>> a.nanmedian()
          ivy.array(3.)
      >>> a.nanmedian(axis=0)
          ivy.array([6.5, 2. , 2.5])
      """
      return ivy.nanmedian(
          self._data,
          axis=axis,
          keepdims=keepdims,
          overwrite_input=overwrite_input,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/experimental/statistical.py
imports: import abc
  from typing import Optional, Union, Tuple, Sequence
  import ivy
label_desc: Use image node_id 8 for calling the _ArrayWithStatisticalExperimental obj's underlying member method code with example usage: obj.nanmedian() and returns: ivy